1/10/2019
Statistical methods of:
At the end of the course students will be able to:
Create maps and other data visualization products with spatial data,
Identify differences between the three common spatial data types: point process, geostatistical, and areal data.
Use statistical software and either Bayesian or classical statistical techniques to analyze spatial point process, geostatistical, and areal data structures.
Implement version control tools, such as git and github, on spatial data analyses.
Analysis, data visualization, and version control procedures will be implemented with:
15% of your grade will be determined by weekly quizzes to be completed prior to class on Tuesdays.
35% of your grade will be determined by weekly homework assignments. Collaboration is encouraged on homework assignments, but everyone should complete their own assignments.
25% of your grade will be determined by a midterm project that focuses on geostatistical data. The midterm project will have a written component and an oral presentation and will likely be due the week prior to spring break.
25% of your grade will be determined by a final project that focuses on either point process or areal data. The final project will have a written component and an oral presentation and will due the finals week.
An updated course schedule will be maintained on the course website.
Introduction: Git, R Studio, Spatial Data Structures, and Data Viz: Week of January 7 - Week of January 14
Point-Referenced Data Week of January 21 - Week of February 18
Areal Data Week of February 25 - Week of March 4
Midterm Project Week of March 11
Areal Data Continued Week of March 25 - Week of April 1
Point Process Data Week of April 8 - Week of April 22
Final Project Week of April 29: Project presentations May 3 from 2 - 3:50
Researchers in many fields are faced with analyzing data with a spatial component. These analyses typically include:
The class will focus on data visualization, modeling, computing, and data analysis.
After a few classes concerned with preliminary concepts: git, github, R, R Studio, and spatial data visualization; the course is organized into three components.
Defining features: continuous surface measured at fixed locations. The observation is random.
source: MT DEQ, http://svc.mt.gov/deq/todaysair/
Question, how do we go from points to a surface map?
source: airnow.gov
Defining features: random observation measured at well defined subsets, such as a city or state.
Question: How could spatial information be incorporated with this data structure?
Defining features: the location of the observation is random, such as the location of a plant.
source: MT FWP
For this class, assignments will be turned in via github. Quiz 1 will be due before our next class.
This will require:
git --version in the terminal or command line.Next, you can click on this link https://classroom.github.com/a/E7XYvjdt, which will create a private repository for your quiz. All you will need to do is edit the README.md file to answer the question AND THEN commit changes at the bottom of the page. We will cover more details about git next week.